Prompt engineering best practices

Craft detailed and direct instructions

Strategy 1: Use delimiters such as , “““, < >, <tag> </tag> to distinguish different sections of the input. This helps in structuring your input effectively and preventing prompt errors. For instance, using the delimiters to specify the text to be summarized.

Strategy 2: Request for a structured output. This could be in a JSON format, which can easily be converted into a list or dictionary in Python later on.

Strategy 3: Confirm whether conditions are met. The prompt can be designed to verify assumptions first. This is particularly helpful when dealing with edge cases. For example, if the input text doesn’t contain any instructions, you can instruct the model to write “No steps provided”.

Strategy 4: Leverage few-shot prompting. Provide the model with successful examples of completed tasks, then ask the model to carry out a similar task.

Allow the model time to ‘Think’

Strategy 1: Detail the steps needed to complete a task and demand output in a specified format. For complex tasks, breaking them down into smaller steps can be beneficial, just as humans often find step-by-step instructions helpful. You can ask the model to follow a logical sequence or chain of reasoning before arriving at the final answer.

Strategy 2: Instruct the model to work out its solution before jumping to a conclusion. This helps the model in thoroughly processing the task at hand before delivering the output.

Opt for the latest model

To attain optimal results, it is advisable to use the most advanced models.

Provide detailed descriptions

Clarity is crucial. Be specific and descriptive about the required context, outcome, length, format, style, etc. For instance, instead of simply requesting a poem about OpenAI, specify details like poem length, style, and a particular theme, such as a recent product launch.

Use examples to illustrate desired output format

The model responds better to specific format requirements shown through examples. This approach also simplifies the process of parsing multiple outputs programmatically

Start with zero-shot, then few-shot, and finally fine-tune

For complex tasks, start with zero-shot, then proceed with few-shot techniques. If these methods don’t yield satisfactory results, consider fine-tuning the model.

Eliminate vague and unnecessary descriptions

Precision is essential. Avoid vague and “fluffy” descriptions. For instance, instead of saying, “The description should be fairly short,” provide a clear guideline such as, “Use a 3 to 5 sentence paragraph to describe this product.”

Give direct instructions over prohibitions

Instead of telling the model what not to do, instruct it on what to do. For instance, in a customer service conversation scenario, instruct the model to diagnose the problem and suggest a solution, avoiding any questions related to personally identifiable information (PII).

Use leading words for code generation

For code generation tasks, nudge the model towards a particular pattern using leading words. This might include using words like ‘import’ to hint the model that it should start writing in Python, or ‘SELECT’ for initiating a SQL statement.

Applications of prompt engineering

Program-aided Language Model (PAL)

Program-aided language models in prompt engineering involve integrating programmatic instructions and structures to enhance the capabilities of language models. By incorporating additional programming logic and constraints, PAL enables more precise and context-aware responses. This approach allows developers to guide the model’s behavior, specify the desired output format, provide relevant examples, and refine prompts based on intermediate results. By leveraging programmatic guidance, PAL techniques empower language models to generate more accurate and tailored responses, making them valuable tools for a wide range of applications in natural language processing.

Here is an example of how PAL can be applied in prompt engineering:

Prompt:

Given a list of numbers, compute the sum of all even numbers.

Input: [2, 5, 8, 10, 3, 6]

Output: The sum of all even numbers is 26.

In this example, the prompt includes a programmatic instruction to compute the sum of even numbers in a given list. By providing this specific task and format, the language model guided by PAL techniques can generate a response that precisely fulfills the desired computation. The integration of programmatic logic and instructions in the prompt ensures accurate and contextually appropriate results.

Generating data

Generating data is an important application of prompt engineering with large language models (LLMs). LLMs have the ability to generate coherent and contextually relevant text, which can be leveraged to create synthetic data for various purposes.

For example, in natural language processing tasks, generating data using LLMs can be valuable for training and evaluating models. By designing prompts that instruct the LLM to generate specific types of data, such as question-answer pairs, text summaries, or dialogue interactions, researchers and practitioners can create large volumes of labeled training data. This synthetic data can then be used to train and improve NLP models, as well as to evaluate their performance.

Here is an example:

Prompt:

Generate 100 question-answer pairs about famous landmarks.

Using this prompt, the LLM can generate a diverse set of question-answer pairs related to famous landmarks around the world. The generated data can be used to enhance questionanswering models or to augment existing datasets for training and evaluation.

By employing prompt engineering techniques, researchers and developers can effectively utilize LLMs to generate data that aligns with their specific needs, enabling them to conduct experiments, evaluate models, and advance various domains of research.

Generating code

Generating code is another application of prompt engineering with large language models. LLMs can be prompted to generate code snippets, functions, or even entire programs, which can be valuable in software development, automation, and programming education.

For example, let’s consider a scenario where a developer wants to generate a Python function that calculates the factorial of a number:

Prompt:

Write a Python function named "factorial" that takes an integer as input and returns its factorial.

By providing this specific prompt to the LLM, it can generate code that implements the factorial function in Python:

Generated Code: def factorial(n): if n == 0 or n == 1: return 1 else: return n * factorial(n - 1)

The generated code demonstrates the recursive implementation of the factorial function in Python.

Prompt engineering allows developers to design prompts with clear instructions and specifications, such as function names, input requirements, and desired output formats. By carefully crafting prompts, LLMs can be guided to generate code snippets tailored to specific programming tasks or requirements.

This application of prompt engineering can be highly beneficial for developers seeking assistance in code generation, automating repetitive tasks, or even for educational purposes where learners can explore different code patterns and learn from the generated examples.

Risks associated with prompting and solutions

As we harness the power of large language models and explore their capabilities, it is important to acknowledge the risks and potential misuses associated with prompting. While well-crafted prompts can yield impressive results, it is crucial to understand the potential pitfalls and safety considerations when using LLMs for real-world applications.

This section sheds light on the risks and misuses of LLMs, particularly through techniques like prompt injections. It also addresses harmful behaviors that may arise and provides insights into mitigating these risks through effective prompting techniques. Additionally, topics such as generalizability, calibration, biases, social biases, and factuality are explored to foster a comprehensive understanding of the challenges involved in working with LLMs.

By recognizing these risks and adopting responsible practices, we can navigate the evolving landscape of LLM applications while promoting ethical and safe use of these powerful language models.

Adversarial prompting

Adversarial prompting refers to the intentional manipulation of prompts to exploit vulnerabilities or biases in language models, resulting in unintended or harmful outputs. Adversarial prompts aim to trick or deceive the model into generating misleading, biased, or inappropriate responses.

Prompt injection: Prompt injection is a technique used in adversarial prompting where additional instructions or content is inserted into the prompt to influence the model’s behavior. By injecting specific keywords, phrases, or instructions, the model’s output can be manipulated to produce desired or undesired outcomes. Prompt injection can be used to introduce biases, generate offensive or harmful content, or manipulate the model’s understanding of the task.

Prompt leaking: Prompt leaking occurs when sensitive or confidential information unintentionally gets exposed in the model’s response. This can happen when the model incorporates parts of the prompt, including personally identifiable information, into its generated output. Prompt leaking poses privacy and security risks, as it may disclose sensitive data to unintended recipients or expose vulnerabilities in the model’s handling of input prompts.

Jailbreaking: In the context of prompt engineering, jailbreaking refers to bypassing or overriding safety mechanisms put in place to restrict or regulate the behavior of language models. It involves manipulating the prompt in a way that allows the model to generate outputs that may be inappropriate, unethical, or against the intended guidelines. Jailbreaking can lead to the generation of offensive content, misinformation, or other undesirable outcomes.

Overall, adversarial prompting techniques like prompt injection, prompt leaking, and jailbreaking highlight the importance of responsible and ethical prompt engineering practices. It is essential to be aware of the potential risks and vulnerabilities associated with language models and to take precautions to mitigate these risks while ensuring the safe and responsible use of these powerful AI systems.

Defense tactics for adversarial prompting

Add defense in the instruction: One defense tactic is to explicitly enforce the desired behavior through the instruction given to the model. While this approach is not foolproof, it emphasizes the power of well-crafted prompts in guiding the model towards the intended output.

Parameterize prompt components: Inspired by techniques used in SQL injection, one potential solution is to parameterize different components of the prompt, separating instructions from inputs and handling them differently. This approach can lead to cleaner and safer solutions, although it may come with some trade-offs in terms of flexibility.

Quotes and additional formatting: Escaping or quoting input strings can provide a workaround to prevent certain prompt injections. This tactic, suggested by Riley, helps maintain robustness across phrasing variations and highlights the importance of proper formatting and careful consideration of prompt structure.

Adversarial prompt detector: Language models themselves can be leveraged to detect and filter out adversarial prompts. By fine-tuning or training an LLM specifically for detecting such prompts, it is possible to incorporate an additional layer of defense to mitigate the impact of adversarial inputs.

Selecting model types: Choosing the appropriate model type can also contribute to defense against prompt injections. For certain tasks, using fine-tuned models or creating k-shot prompts for non-instruct models can be effective. Fine-tuning a model on a large number of examples can help improve robustness and accuracy, reducing reliance on instruction-based models.

Guardrails and safety measures: Some language models, like ChatGPT, incorporate guardrails and safety measures to prevent malicious or dangerous prompts. While these measures provide a level of protection, they are not perfect and can still be susceptible to novel adversarial prompts. It is important to recognize the trade-off between safety constraints and desired behaviors.

Factuality

It is worth noting that the field of prompt engineering and defense against adversarial prompting is an evolving area, and more research and development are needed to establish robust and comprehensive defense tactics against text-based attacks. Factuality is a significant risk in prompting as LLMs can generate responses that appear coherent and convincing but may lack accuracy. To address this, there are several solutions that can be employed:

Provide ground truth: Including reliable and factual information as part of the context can help guide the model to generate more accurate responses. This can involve referencing related articles, excerpts from reliable sources, or specific sections from Wikipedia entries. By incorporating verified information, the model is less likely to produce fabricated or inconsistent responses.

Control response diversity: Modifying the probability parameters of the model can influence the diversity of its responses. By decreasing the probability values, the model can be guided towards generating more focused and factually accurate answers. Additionally, explicitly instructing the model to acknowledge uncertainty by admitting when it doesn’t possess the required knowledge can also mitigate the risk of generating false information.

Provide examples in the prompt: Including a combination of questions and responses in the prompt can guide the model to differentiate between topics it is familiar with and those it is not. By explicitly demonstrating examples of both known and unknown information, the model can better understand the boundaries of its knowledge and avoid generating false or speculative responses.

These solutions help address the risk of factuality in prompting by promoting more accurate and reliable output from LLMs. However, it is important to continuously evaluate and refine the prompt engineering strategies to ensure the best possible balance between generating coherent responses and maintaining factual accuracy

Biases

Biases in LLMs pose a significant risk as they can lead to the generation of problematic and biased content. These biases can adversely impact the performance of the model in downstream tasks and perpetuate harmful stereotypes or discriminatory behavior. To address this, it is essential to implement appropriate solutions:

Effective prompting strategies: Crafting well-designed prompts can help mitigate biases to some extent. By providing specific instructions and context that encourage fairness and inclusivity, the model can be guided to generate more unbiased responses. Additionally, incorporating diverse and representative examples in the prompt can help the model learn from a broader range of perspectives, reducing the likelihood of biased output.

Moderation and filtering: Implementing robust moderation and filtering mechanisms can help identify and mitigate biased content generated by LLMs. This involves developing systems that can detect and flag potentially biased or harmful outputs in real-time. Human reviewers or content moderation teams can then review and address any problematic content, ensuring that biased or discriminatory responses are not propagated.

Diverse training data: Training LLMs on diverse datasets that encompass a wide range of perspectives and experiences can help reduce biases. By exposing the model to a more comprehensive set of examples, it learns to generate responses that are more balanced and representative. Regularly updating and expanding the training data with diverse sources can further enhance the model’s ability to generate unbiased content.

Post-processing and debiasing techniques:Applying post-processing techniques to the generated output can help identify and mitigate biases. These techniques involve analyzing the model’s responses for potential biases and adjusting them to ensure fairness and inclusivity. Debiasing methods can be employed to retrain the model, explicitly addressing and reducing biases in its output.

It is important to note that addressing biases in LLMs is an ongoing challenge, and no single solution can completely eliminate biases. It requires a combination of thoughtful prompt engineering, robust moderation practices, diverse training data, and continuous improvement of the underlying models. Close collaboration between researchers, practitioners, and communities is crucial to develop effective strategies and ensure responsible and unbiased use of LLMs.

Endnote

The future of language model learning is deeply intertwined with the ongoing evolution of prompt engineering. As we stand on the threshold of this technological transformation, the vast and untapped potential of prompt engineering is coming into focus. It serves as a bridge between the complex world of AI and the intricacy of human language, facilitating communication that is not just effective, but also intuitive and humanlike.

In the realm of LLM, well-engineered prompts play a pivotal role. They are the steering wheel guiding the direction of machine learning models, helping them navigate through the maze of human languages with precision and understanding. As AI technologies become more sophisticated and integrated into our daily lives – from voice assistants on our phones to AI chatbots in customer service – the role of prompt engineering in crafting nuanced, contextaware prompts have become more important than ever.

Moreover, as the field of LLM expands into newer territories like automated content creation, data analysis, and even healthcare diagnostics, prompt engineering will be at the helm, guiding the course. It’s not just about crafting questions for AI to answer; it’s about understanding the context, the intent, and the desired outcome, and encoding all of that into a concise, effective prompt.

Investing time, research, and resources into prompt engineering today will have a ripple effect on our AI-enabled future. It will fuel advancements in LLM and lay the groundwork for AI technologies we can’t even envision yet. The future of LLM, and indeed, the future of our increasingly AI-integrated world, rests in the hands of skilled prompt engineers.

Enhance your LLM’s power and performance with prompt engineering. To harness the power of prompt engineering, hire LeewayHertz’s LLM development services today and ensure business success in today’s AI-centric world!